Core Concepts
Deep learning with cartogram aids demand prediction in open systems.
Abstract
The article discusses the challenges of predicting temporal patterns in shared transport systems like public bicycles due to their openness and imbalanced usage. It introduces a deep learning framework using cartogram approaches to predict rental and return patterns, showcasing improved accuracy across different time scales.
Introduction
Predicting temporal patterns is challenging due to nuanced trajectories.
Shared transport systems face difficulties in predicting rental and return patterns.
Data Construction and Prediction Method
Spatio-temporal demand patterns of public bicycles are analyzed using deep learning models.
The study focuses on Seoul's open system, utilizing CNN for prediction alongside graph neural networks.
Rental-and-return data in Seoul
Time series data collection for rentals and returns hourly over two years.
City map division into grids for computational efficiency.
Node-feature matrix
Matrix size considerations for efficient data processing.
Stats
The total number of rental stations is 1,538 for 2018 and 1,554 for 2019.
Time series data was collected every hour from January 1, 2018, to December 31, 2019.